An extensible speaker identification sidekit in Python

• Published in: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5095 - 5099
• Main Authors: Larcher, Anthony, Lee, Kong Aik, Meignier, Sylvain
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.03.2016
• Subjects: Algorithms; Electronics; Feature extraction; Open source software; open-source; Panels; python; Source code; Speaker recognition; Speech; toolkit; Tutorials; Visualization
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2016.7472648

SIDEKIT is a new open-source Python toolkit that includes a large panel of state-of-the-art components and allow a rapid prototyping of an end-to-end speaker recognition system. For each step from front-end feature extraction, normalization, speech activity detection, modelling, scoring and visualization, SIDEKIT offers a wide range of standard algorithms and flexible interfaces. The use of a single efficient programming and scripting language (Python in this case), and the limited dependencies, facilitate the deployment for industrial applications and extension to include new algorithms as part of the whole tool-chain provided by SIDEKIT. Performance of SIDEKIT is demonstrated on two standard evaluation tasks, namely the RSR2015 and NIST-SRE 2010.